Overview

Dataset statistics

Number of variables26
Number of observations2240
Missing cells0
Missing cells (%)0.0%
Duplicate rows176
Duplicate rows (%)7.9%
Total size in memory455.1 KiB
Average record size in memory208.1 B

Variable types

Categorical11
Numeric15

Alerts

Dataset has 176 (7.9%) duplicate rowsDuplicates
Income is highly correlated with Kidhome and 10 other fieldsHigh correlation
Kidhome is highly correlated with Income and 4 other fieldsHigh correlation
MntWines is highly correlated with Income and 9 other fieldsHigh correlation
MntFruits is highly correlated with Income and 7 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 9 other fieldsHigh correlation
MntFishProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntSweetProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntGoldProds is highly correlated with Income and 8 other fieldsHigh correlation
NumWebPurchases is highly correlated with Income and 5 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 10 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 9 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 1 other fieldsHigh correlation
Income is highly correlated with Kidhome and 8 other fieldsHigh correlation
Kidhome is highly correlated with Income and 1 other fieldsHigh correlation
MntWines is highly correlated with Income and 4 other fieldsHigh correlation
MntFruits is highly correlated with Income and 3 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 6 other fieldsHigh correlation
MntFishProducts is highly correlated with Income and 4 other fieldsHigh correlation
MntSweetProducts is highly correlated with Income and 3 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 1 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 6 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 3 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 2 other fieldsHigh correlation
Income is highly correlated with MntWines and 3 other fieldsHigh correlation
Kidhome is highly correlated with NumCatalogPurchasesHigh correlation
MntWines is highly correlated with Income and 4 other fieldsHigh correlation
MntFruits is highly correlated with MntMeatProducts and 2 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 7 other fieldsHigh correlation
MntFishProducts is highly correlated with MntFruits and 3 other fieldsHigh correlation
MntSweetProducts is highly correlated with MntFruits and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 2 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 5 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 4 other fieldsHigh correlation
Income is highly correlated with Kidhome and 7 other fieldsHigh correlation
Kidhome is highly correlated with Income and 4 other fieldsHigh correlation
Teenhome is highly correlated with NumDealsPurchasesHigh correlation
MntWines is highly correlated with Income and 9 other fieldsHigh correlation
MntFruits is highly correlated with MntWines and 3 other fieldsHigh correlation
MntMeatProducts is highly correlated with Income and 3 other fieldsHigh correlation
MntFishProducts is highly correlated with MntWines and 5 other fieldsHigh correlation
MntSweetProducts is highly correlated with Kidhome and 4 other fieldsHigh correlation
MntGoldProds is highly correlated with MntWines and 4 other fieldsHigh correlation
NumDealsPurchases is highly correlated with Teenhome and 2 other fieldsHigh correlation
NumWebPurchases is highly correlated with MntWines and 3 other fieldsHigh correlation
NumCatalogPurchases is highly correlated with Income and 5 other fieldsHigh correlation
NumStorePurchases is highly correlated with Income and 10 other fieldsHigh correlation
NumWebVisitsMonth is highly correlated with Income and 3 other fieldsHigh correlation
AcceptedCmp4 is highly correlated with MntWinesHigh correlation
AcceptedCmp5 is highly correlated with Income and 2 other fieldsHigh correlation
AcceptedCmp1 is highly correlated with Income and 1 other fieldsHigh correlation
Recency has 28 (1.2%) zeros Zeros
MntFruits has 400 (17.9%) zeros Zeros
MntFishProducts has 384 (17.1%) zeros Zeros
MntSweetProducts has 419 (18.7%) zeros Zeros
MntGoldProds has 61 (2.7%) zeros Zeros
NumDealsPurchases has 46 (2.1%) zeros Zeros
NumWebPurchases has 49 (2.2%) zeros Zeros
NumCatalogPurchases has 586 (26.2%) zeros Zeros

Reproduction

Analysis started2021-10-15 16:10:19.897425
Analysis finished2021-10-15 16:11:00.178792
Duration40.28 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
3
1127 
5
486 
4
370 
2
203 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row5

Common Values

ValueCountFrequency (%)
31127
50.3%
5486
21.7%
4370
 
16.5%
2203
 
9.1%
154
 
2.4%

Length

2021-10-15T13:11:00.500792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:00.580793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
31127
50.3%
5486
21.7%
4370
 
16.5%
2203
 
9.1%
154
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Married
864 
Together
580 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.073214286
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowTogether
4th rowTogether
5th rowMarried

Common Values

ValueCountFrequency (%)
Married864
38.6%
Together580
25.9%
Single480
21.4%
Divorced232
 
10.4%
Widow77
 
3.4%
Alone3
 
0.1%
Absurd2
 
0.1%
YOLO2
 
0.1%

Length

2021-10-15T13:11:00.690789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:00.790794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
married864
38.6%
together580
25.9%
single480
21.4%
divorced232
 
10.4%
widow77
 
3.4%
alone3
 
0.1%
absurd2
 
0.1%
yolo2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Income
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1974
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51969.8614
Minimum1730
Maximum162397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:00.933790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19101.05
Q135538.75
median51741.5
Q368275.75
95-th percentile83892.3
Maximum162397
Range160667
Interquartile range (IQR)32737

Descriptive statistics

Standard deviation21405.80454
Coefficient of variation (CV)0.4118888132
Kurtosis0.7554590646
Mean51969.8614
Median Absolute Deviation (MAD)16380.5
Skewness0.3493017456
Sum116412489.5
Variance458208467.8
MonotonicityNot monotonic
2021-10-15T13:11:01.098788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51969.861425
 
1.1%
750012
 
0.5%
358604
 
0.2%
377603
 
0.1%
801343
 
0.1%
460983
 
0.1%
484323
 
0.1%
399223
 
0.1%
638413
 
0.1%
674453
 
0.1%
Other values (1964)2178
97.2%
ValueCountFrequency (%)
17301
< 0.1%
24471
< 0.1%
35021
< 0.1%
40231
< 0.1%
44281
< 0.1%
48611
< 0.1%
53051
< 0.1%
56481
< 0.1%
65601
< 0.1%
68351
< 0.1%
ValueCountFrequency (%)
1623971
< 0.1%
1608031
< 0.1%
1577331
< 0.1%
1572431
< 0.1%
1571461
< 0.1%
1569241
< 0.1%
1539241
< 0.1%
1137341
< 0.1%
1054711
< 0.1%
1026921
< 0.1%

Kidhome
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1293 
1
899 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Length

2021-10-15T13:11:01.247790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:01.324803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01293
57.7%
1899
40.1%
248
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Teenhome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1158 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Length

2021-10-15T13:11:01.407790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:01.482804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01158
51.7%
11030
46.0%
252
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Dt_Customer
Real number (ℝ≥0)

Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3017.043304
Minimum2505
Maximum3568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:01.588790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2505
5-th percentile2662
Q12845.75
median3018
Q33190.25
95-th percentile3382
Maximum3568
Range1063
Interquartile range (IQR)344.5

Descriptive statistics

Standard deviation232.2298926
Coefficient of variation (CV)0.07697267464
Kurtosis-0.6379570308
Mean3017.043304
Median Absolute Deviation (MAD)172.5
Skewness0.005415271942
Sum6758177
Variance53930.723
MonotonicityNot monotonic
2021-10-15T13:11:01.759792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333212
 
0.5%
323211
 
0.5%
316511
 
0.5%
250611
 
0.5%
297810
 
0.4%
270310
 
0.4%
27219
 
0.4%
27639
 
0.4%
31789
 
0.4%
28429
 
0.4%
Other values (653)2139
95.5%
ValueCountFrequency (%)
25051
 
< 0.1%
250611
0.5%
25072
 
0.1%
25085
0.2%
25093
 
0.1%
25102
 
0.1%
25351
 
< 0.1%
25368
0.4%
25374
 
0.2%
25382
 
0.1%
ValueCountFrequency (%)
35684
0.2%
35671
 
< 0.1%
35665
0.2%
35652
 
0.1%
35644
0.2%
35373
0.1%
35361
 
< 0.1%
35351
 
< 0.1%
35342
 
0.1%
35332
 
0.1%

Recency
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.109375
Minimum0
Maximum99
Zeros28
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:01.932788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.96245281
Coefficient of variation (CV)0.5897540502
Kurtosis-1.201896799
Mean49.109375
Median Absolute Deviation (MAD)25
Skewness-0.001986658634
Sum110005
Variance838.8236727
MonotonicityNot monotonic
2021-10-15T13:11:02.098788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5637
 
1.7%
3032
 
1.4%
5432
 
1.4%
4631
 
1.4%
9230
 
1.3%
4930
 
1.3%
6530
 
1.3%
329
 
1.3%
2929
 
1.3%
7129
 
1.3%
Other values (90)1931
86.2%
ValueCountFrequency (%)
028
1.2%
124
1.1%
228
1.2%
329
1.3%
427
1.2%
515
0.7%
621
0.9%
712
0.5%
825
1.1%
924
1.1%
ValueCountFrequency (%)
9917
0.8%
9822
1.0%
9720
0.9%
9625
1.1%
9519
0.8%
9426
1.2%
9321
0.9%
9230
1.3%
9118
0.8%
9020
0.9%

MntWines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.9357143
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:02.265789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123.75
median173.5
Q3504.25
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.5973926
Coefficient of variation (CV)1.107462456
Kurtosis0.5987435935
Mean303.9357143
Median Absolute Deviation (MAD)164.5
Skewness1.175770564
Sum680816
Variance113297.8047
MonotonicityNot monotonic
2021-10-15T13:11:02.551795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.9%
540
 
1.8%
137
 
1.7%
637
 
1.7%
433
 
1.5%
830
 
1.3%
330
 
1.3%
928
 
1.2%
1225
 
1.1%
1024
 
1.1%
Other values (766)1914
85.4%
ValueCountFrequency (%)
013
 
0.6%
137
1.7%
242
1.9%
330
1.3%
433
1.5%
540
1.8%
637
1.7%
722
1.0%
830
1.3%
928
1.2%
ValueCountFrequency (%)
14931
< 0.1%
14922
0.1%
14861
< 0.1%
14782
0.1%
14621
< 0.1%
14591
< 0.1%
14491
< 0.1%
13961
< 0.1%
13941
< 0.1%
13791
< 0.1%

MntFruits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.30223214
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:02.720791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.77343376
Coefficient of variation (CV)1.51216952
Kurtosis4.050976251
Mean26.30223214
Median Absolute Deviation (MAD)8
Skewness2.102063305
Sum58917
Variance1581.926033
MonotonicityNot monotonic
2021-10-15T13:11:02.890805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0400
 
17.9%
1162
 
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
767
 
3.0%
565
 
2.9%
662
 
2.8%
1250
 
2.2%
848
 
2.1%
Other values (148)1046
46.7%
ValueCountFrequency (%)
0400
17.9%
1162
7.2%
2120
 
5.4%
3116
 
5.2%
4104
 
4.6%
565
 
2.9%
662
 
2.8%
767
 
3.0%
848
 
2.1%
935
 
1.6%
ValueCountFrequency (%)
1992
0.1%
1971
 
< 0.1%
1943
0.1%
1932
0.1%
1901
 
< 0.1%
1891
 
< 0.1%
1852
0.1%
1841
 
< 0.1%
1833
0.1%
1811
 
< 0.1%

MntMeatProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.95
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:03.060792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.1
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.7153725
Coefficient of variation (CV)1.351993846
Kurtosis5.516724101
Mean166.95
Median Absolute Deviation (MAD)59
Skewness2.083233113
Sum373968
Variance50947.42939
MonotonicityNot monotonic
2021-10-15T13:11:03.214788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753
 
2.4%
550
 
2.2%
1149
 
2.2%
846
 
2.1%
643
 
1.9%
1040
 
1.8%
340
 
1.8%
938
 
1.7%
1636
 
1.6%
1235
 
1.6%
Other values (548)1810
80.8%
ValueCountFrequency (%)
01
 
< 0.1%
114
 
0.6%
230
1.3%
340
1.8%
430
1.3%
550
2.2%
643
1.9%
753
2.4%
846
2.1%
938
1.7%
ValueCountFrequency (%)
17252
0.1%
16221
< 0.1%
16071
< 0.1%
15821
< 0.1%
9841
< 0.1%
9811
< 0.1%
9741
< 0.1%
9681
< 0.1%
9611
< 0.1%
9512
0.1%

MntFishProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.52544643
Minimum0
Maximum259
Zeros384
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:03.376791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.05
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.6289794
Coefficient of variation (CV)1.45578493
Kurtosis3.096460912
Mean37.52544643
Median Absolute Deviation (MAD)12
Skewness1.919768971
Sum84057
Variance2984.325391
MonotonicityNot monotonic
2021-10-15T13:11:03.543792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0384
 
17.1%
2156
 
7.0%
3130
 
5.8%
4108
 
4.8%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
1348
 
2.1%
1247
 
2.1%
Other values (172)1106
49.4%
ValueCountFrequency (%)
0384
17.1%
110
 
0.4%
2156
7.0%
3130
 
5.8%
4108
 
4.8%
51
 
< 0.1%
682
 
3.7%
766
 
2.9%
858
 
2.6%
1055
 
2.5%
ValueCountFrequency (%)
2591
 
< 0.1%
2583
0.1%
2541
 
< 0.1%
2531
 
< 0.1%
2503
0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2421
 
< 0.1%
2402
0.1%
2372
0.1%

MntSweetProducts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.06294643
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:03.706788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.28049849
Coefficient of variation (CV)1.525351225
Kurtosis4.376548261
Mean27.06294643
Median Absolute Deviation (MAD)8
Skewness2.136080712
Sum60621
Variance1704.079555
MonotonicityNot monotonic
2021-10-15T13:11:03.868792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0419
 
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
1245
 
2.0%
Other values (167)1062
47.4%
ValueCountFrequency (%)
0419
18.7%
1161
 
7.2%
2128
 
5.7%
3101
 
4.5%
482
 
3.7%
565
 
2.9%
664
 
2.9%
757
 
2.5%
856
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
2631
 
< 0.1%
2621
 
< 0.1%
1981
 
< 0.1%
1971
 
< 0.1%
1961
 
< 0.1%
1951
 
< 0.1%
1943
0.1%
1923
0.1%
1911
 
< 0.1%
1892
0.1%

MntGoldProds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.021875
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:04.028792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.05
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.16743891
Coefficient of variation (CV)1.185034461
Kurtosis3.55170925
Mean44.021875
Median Absolute Deviation (MAD)18
Skewness1.886105609
Sum98609
Variance2721.441683
MonotonicityNot monotonic
2021-10-15T13:11:04.184795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173
 
3.3%
470
 
3.1%
369
 
3.1%
563
 
2.8%
1263
 
2.8%
262
 
2.8%
061
 
2.7%
657
 
2.5%
754
 
2.4%
1049
 
2.2%
Other values (203)1619
72.3%
ValueCountFrequency (%)
061
2.7%
173
3.3%
262
2.8%
369
3.1%
470
3.1%
563
2.8%
657
2.5%
754
2.4%
840
1.8%
944
2.0%
ValueCountFrequency (%)
3621
< 0.1%
3211
< 0.1%
2911
< 0.1%
2621
< 0.1%
2491
< 0.1%
2481
< 0.1%
2471
< 0.1%
2461
< 0.1%
2451
< 0.1%
2422
0.1%

NumDealsPurchases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.325
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:04.334803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.932237501
Coefficient of variation (CV)0.8310698928
Kurtosis8.936914321
Mean2.325
Median Absolute Deviation (MAD)1
Skewness2.418569388
Sum5208
Variance3.73354176
MonotonicityNot monotonic
2021-10-15T13:11:04.449789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
046
 
2.1%
740
 
1.8%
814
 
0.6%
98
 
0.4%
Other values (5)24
 
1.1%
ValueCountFrequency (%)
046
 
2.1%
1970
43.3%
2497
22.2%
3297
 
13.3%
4189
 
8.4%
594
 
4.2%
661
 
2.7%
740
 
1.8%
814
 
0.6%
98
 
0.4%
ValueCountFrequency (%)
157
 
0.3%
133
 
0.1%
124
 
0.2%
115
 
0.2%
105
 
0.2%
98
 
0.4%
814
 
0.6%
740
1.8%
661
2.7%
594
4.2%

NumWebPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.084821429
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:04.567792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.778714147
Coefficient of variation (CV)0.680253518
Kurtosis5.703128364
Mean4.084821429
Median Absolute Deviation (MAD)2
Skewness1.382794296
Sum9150
Variance7.721252313
MonotonicityNot monotonic
2021-10-15T13:11:04.820804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2373
16.7%
1354
15.8%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
049
 
2.2%
Other values (5)91
 
4.1%
ValueCountFrequency (%)
049
 
2.2%
1354
15.8%
2373
16.7%
3336
15.0%
4280
12.5%
5220
9.8%
6205
9.2%
7155
6.9%
8102
 
4.6%
975
 
3.3%
ValueCountFrequency (%)
272
 
0.1%
251
 
< 0.1%
231
 
< 0.1%
1144
 
2.0%
1043
 
1.9%
975
 
3.3%
8102
4.6%
7155
6.9%
6205
9.2%
5220
9.8%

NumCatalogPurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.662053571
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:04.935791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.923100656
Coefficient of variation (CV)1.098062296
Kurtosis8.047436789
Mean2.662053571
Median Absolute Deviation (MAD)2
Skewness1.880988778
Sum5963
Variance8.544517442
MonotonicityNot monotonic
2021-10-15T13:11:05.056790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
1048
 
2.1%
Other values (4)65
 
2.9%
ValueCountFrequency (%)
0586
26.2%
1497
22.2%
2276
12.3%
3184
 
8.2%
4182
 
8.1%
5140
 
6.2%
6128
 
5.7%
779
 
3.5%
855
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
283
 
0.1%
221
 
< 0.1%
1119
 
0.8%
1048
 
2.1%
942
 
1.9%
855
 
2.5%
779
3.5%
6128
5.7%
5140
6.2%
4182
8.1%

NumStorePurchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.790178571
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:05.170804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.250958146
Coefficient of variation (CV)0.5614607746
Kurtosis-0.6220482771
Mean5.790178571
Median Absolute Deviation (MAD)2
Skewness0.7022372855
Sum12970
Variance10.56872886
MonotonicityNot monotonic
2021-10-15T13:11:05.294791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3490
21.9%
4323
14.4%
2223
10.0%
5212
9.5%
6178
 
7.9%
8149
 
6.7%
7143
 
6.4%
10125
 
5.6%
9106
 
4.7%
12105
 
4.7%
Other values (4)186
 
8.3%
ValueCountFrequency (%)
015
 
0.7%
17
 
0.3%
2223
10.0%
3490
21.9%
4323
14.4%
5212
9.5%
6178
 
7.9%
7143
 
6.4%
8149
 
6.7%
9106
 
4.7%
ValueCountFrequency (%)
1383
 
3.7%
12105
 
4.7%
1181
 
3.6%
10125
 
5.6%
9106
 
4.7%
8149
6.7%
7143
6.4%
6178
7.9%
5212
9.5%
4323
14.4%

NumWebVisitsMonth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.316517857
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:05.418789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.42664501
Coefficient of variation (CV)0.4564350341
Kurtosis1.821613827
Mean5.316517857
Median Absolute Deviation (MAD)2
Skewness0.2079255568
Sum11909
Variance5.888606002
MonotonicityNot monotonic
2021-10-15T13:11:05.541791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7393
17.5%
8342
15.3%
6340
15.2%
5281
12.5%
4218
9.7%
3205
9.2%
2202
9.0%
1153
 
6.8%
983
 
3.7%
011
 
0.5%
Other values (6)12
 
0.5%
ValueCountFrequency (%)
011
 
0.5%
1153
 
6.8%
2202
9.0%
3205
9.2%
4218
9.7%
5281
12.5%
6340
15.2%
7393
17.5%
8342
15.3%
983
 
3.7%
ValueCountFrequency (%)
203
 
0.1%
192
 
0.1%
171
 
< 0.1%
142
 
0.1%
131
 
< 0.1%
103
 
0.1%
983
 
3.7%
8342
15.3%
7393
17.5%
6340
15.2%

AcceptedCmp3
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-10-15T13:11:05.671789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:05.746791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AcceptedCmp4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2073 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Length

2021-10-15T13:11:05.823791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:05.900803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02073
92.5%
1167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AcceptedCmp5
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2077 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Length

2021-10-15T13:11:05.976790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:06.049792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02077
92.7%
1163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AcceptedCmp1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2096 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Length

2021-10-15T13:11:06.126789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:06.200792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02096
93.6%
1144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AcceptedCmp2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2210 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Length

2021-10-15T13:11:06.276790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:06.356803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02210
98.7%
130
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Complain
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
2219 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Length

2021-10-15T13:11:06.436791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:06.514803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
02219
99.1%
121
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
0
1906 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Length

2021-10-15T13:11:06.590789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-15T13:11:06.664803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01906
85.1%
1334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

Distinct59
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.19419643
Minimum25
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2021-10-15T13:11:06.766792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile33
Q144
median51
Q362
95-th percentile71
Maximum128
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.98406946
Coefficient of variation (CV)0.2296054021
Kurtosis0.7174644425
Mean52.19419643
Median Absolute Deviation (MAD)9
Skewness0.3499438592
Sum116915
Variance143.6179207
MonotonicityNot monotonic
2021-10-15T13:11:06.935792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4589
 
4.0%
5087
 
3.9%
4683
 
3.7%
4979
 
3.5%
4377
 
3.4%
5177
 
3.4%
4874
 
3.3%
5674
 
3.3%
5271
 
3.2%
4769
 
3.1%
Other values (49)1460
65.2%
ValueCountFrequency (%)
252
 
0.1%
265
 
0.2%
273
 
0.1%
285
 
0.2%
2913
0.6%
3015
0.7%
3118
0.8%
3230
1.3%
3329
1.3%
3427
1.2%
ValueCountFrequency (%)
1281
 
< 0.1%
1221
 
< 0.1%
1211
 
< 0.1%
811
 
< 0.1%
801
 
< 0.1%
787
0.3%
777
0.3%
768
0.4%
7516
0.7%
7416
0.7%

Interactions

2021-10-15T13:10:56.805924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:24.244479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.605479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.879478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.043526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.390531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.735525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.996307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.525306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.900404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.429936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.740980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.040076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.185160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.445174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.947924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:24.409481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.756480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.028478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.205523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.549522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.888525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:38.286309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.677308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.074400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.714966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.901078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.197079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.336162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.603166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.084911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:24.648478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.892481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.164505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.476537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.692536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.040523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:38.437311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.814321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.250402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.854962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.037208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.331079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.591161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.749174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.212919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:24.798478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.028480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.296507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.615527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.833527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.178529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:38.591308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.949321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.430923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.990963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.175080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.464091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.730162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.890162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.355924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:24.949477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.172481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.442510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.779527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.981524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.324537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:38.765309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.088309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.642983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.138961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.321079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.613076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.874166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.039160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.499909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.106482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.317481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.585520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:31.929523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:34.138537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.475037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:38.922309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.237312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.827976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.287964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.480078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.758077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.036160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.192163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.638911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.259481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.460479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.737507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.078537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:34.291524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.621037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.081309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.392309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:43.992821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.436963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.631077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:50.907081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.184161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.352160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.770913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.402480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.595481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:29.876507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.217529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:34.433525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.760705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.224309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.541309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.146838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.579968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.766079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.045082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.320159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.493164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:57.901911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.548484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:27.729484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.017506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.361523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:34.573527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:36.912228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.418309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.682308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.309855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.721994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:48.903078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.180076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.455164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.636165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.041909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.702480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.003481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.171507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.506522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:34.858522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.069250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.635311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:41.953309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.479861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:46.871981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.171076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.329076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.601159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:55.918166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.179913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.851477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.156482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.312506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.662524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.010525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.239249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.808308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.107306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.640860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.016983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.317091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.474604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.748158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.072163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.312913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:25.997483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.302492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.460505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.816526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.158537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.381289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:39.941321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.265311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.792917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.159982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.456083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.614594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:53.887166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.215924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.445924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.146480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.441481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.601504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:32.956528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.298537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.523304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.091306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.409878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:44.938939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.300981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.595083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.750126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.023162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.359040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.580912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.296481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.590482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.745528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.101524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.441536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.674712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.232309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.560867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.108938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.446994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.741076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:51.895132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.161165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.509924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:58.725913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:26.461479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:28.744478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:30.905522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:33.252537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:35.594537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:37.836308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:40.381310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:42.745405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:45.288937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:47.600987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:49.901077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:52.051161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:54.312174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-15T13:10:56.664924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-15T13:11:07.248794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-15T13:11:07.603833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-15T13:11:07.953499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-15T13:11:08.279502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-15T13:11:08.535496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-15T13:10:59.165909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-15T13:11:00.002149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

EducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseage
03Single58138.000347658635885461728888381047000000164
13Single46344.011263038111621621125000000067
23Together71613.000297726426491271112142182104000000056
33Together26646.01025702611420103522046000000037
45Married58293.0102826941734311846271555365000000040
54Together62513.001295816520429804214264106000000054
63Divorced55635.0013258342356516450492747376000000050
75Married33454.010299332761056312324048000000036
85Together30351.0103053191402433213029000000147
95Together5648.01127736828061113110020100000071

Last rows

EducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseage
22303Single11012.00001031358224326712333129100000037
22314Single44802.00000033427185310143131020294128000000051
22323Single26816.00000033465051634310034000000035
22333Together51969.86141031732391418811243136000000044
22343Married34421.00001032038133762911027000000047
22353Married61223.000001304646709431824211824729345000000054
22365Together64014.00002125665640603000878257000100075
22373Divorced56981.00000028209190848217321224123136010000040
22384Together69245.0000012821842830214803061265103000000065
22395Married52869.00001132874084361212133147000000167

Duplicate rows

Most frequently occurring

EducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseage# duplicates
263Married18690.00032137761723419111280000000623
273Married18929.000316315320823418110460000000313
383Married39922.01031653029125919136230480000000383
503Married67445.001323363757802172980115961260000000473
1043Together83844.0002871579013134575311911441110010000693
1144Divorced63841.00130996463515100207131193960000000533
01Married20425.01032735412531617220370000000352
11Married28249.00026798019721410120360000000602
21Together22634.000319447223118646121280000000552
31Together24594.0102925941361009110350000000422